Current opinion in structural biology最新文献

This mini-review reports the recent advances in biomolecular simulations, particularly for nucleic acids, and provides the potential effects of the emerging exascale computing on nucleic acid simulations, emphasizing the need for advanced computational strategies to fully exploit this technological frontier. Specifically, we introduce recent breakthroughs in computer architectures for large-scale biomolecular simulations and review the simulation protocols for nucleic acids regarding force fields, enhanced sampling methods, coarse-grained models, and interactions with ligands. We also explore the integration of machine learning methods into simulations, which promises to significantly enhance the predictive modeling of biomolecules and the analysis of complex data generated by the exascale simulations. Finally, we discuss the challenges and perspectives for biomolecular simulations as we enter the dawning exascale computing era.

Microsecond time-resolved cryo-electron microscopy has emerged as a novel approach for directly observing protein dynamics. By providing microsecond temporal and near-atomic spatial resolution, it has the potential to elucidate a wide range of dynamics that were previously inaccessible and therefore, to significantly advance our understanding of protein function. This review summarizes the properties of the laser melting and revitrification process that underlies the technique and describes different experimental implementations. Strategies for initiating and probing dynamics are discussed. Finally, the microsecond time-resolved observation of the capsid dynamics of cowpea chlorotic mottle virus, an icosahedral plant virus, is reviewed, which illustrates important features of the technique as well as its potential.

Microtubule-targeting agents (MTAs) have demonstrated remarkable efficacy as antitumor, antifungal, antiparasitic, and herbicidal agents, finding applications in the clinical, veterinary, and agrochemical industry. Recent advances in tubulin and microtubule structural biology have provided powerful tools that pave the way for the rational design of innovative small-molecule MTAs for future basic and applied life science applications. In this mini-review, we present the current status of the tubulin and microtubule structural biology field, the recent impact it had on the discovery and rational design of MTAs, and exciting avenues for future MTA research.

Artificial intelligence (AI) and high-content imaging (HCI) are contributing to advancements in drug discovery, propelled by the recent progress in deep neural networks. This review highlights AI's role in analysis of HCI data from fixed and live-cell imaging, enabling novel label-free and multi-channel fluorescent screening methods, and improving compound profiling. HCI experiments are rapid and cost-effective, facilitating large data set accumulation for AI model training. However, the success of AI in drug discovery also depends on high-quality data, reproducible experiments, and robust validation to ensure model performance. Despite challenges like the need for annotated compounds and managing vast image data, AI's potential in phenotypic screening and drug profiling is significant. Future improvements in AI, including increased interpretability and integration of multiple modalities, are expected to solidify AI and HCI's role in drug discovery.

New high-performance computing architectures are becoming operative, in addition to exascale computers. Quantum computers (QC) solve optimization problems with unprecedented efficiency and speed, while neuromorphic hardware (NMH) simulates neural network dynamics. Albeit, at the moment, both find no practical use in all atom biomolecular simulations, QC might be exploited in the not-too-far future to simulate systems for which electronic degrees of freedom play a key and intricate role for biological function, whereas NMH might accelerate molecular dynamics simulations with low energy consumption. Machine learning and artificial intelligence algorithms running on NMH and QC could assist in the analysis of data and speed up research. If these implementations are successful, modular supercomputing could further dramatically enhance the overall computing capacity by combining highly optimized software tools into workflows, linking these architectures to exascale computers.

Recent progress in protein structure prediction based on deep learning revolutionized the field of Structural Biology. Beyond single proteins, it also enabled high-throughput prediction of structures of protein–protein interactions. Despite the success in predicting complex structures, large macromolecular assemblies still require specialized approaches. Here we describe recent advances in modeling macromolecular assemblies using integrative and hierarchical approaches. We highlight applications that predict protein–protein interactions and challenges in modeling complexes based on the interaction networks, including the prediction of complex stoichiometry and heterogeneity.

Nanodiscs represent a versatile tool for studies of membrane proteins and protein-membrane interactions under native-like conditions. Multiple variations of the Nanodisc platform, as well as new experimental methods, have been recently developed to understand various aspects of structure, dynamics and functional properties of systems involved in signaling, transport, blood coagulation and many other critically important processes. In this mini-review, we focus on some of these exciting recent developments that utilize the Nanodisc platform.

Recent technological advances have deepened our perception of cellular structure. However, most structural data doesn't originate from intact cells, limiting our understanding of cellular processes. Here, we discuss current and future developments that will bring us towards a structural picture of the cell. Electron cryotomography is the standard bearer, with its ability to provide in cellulo snapshots. Single-particle electron microscopy (of purified biomolecules and of complex mixtures) and covalent crosslinking combined with mass spectrometry also have significant roles to play, as do artificial intelligence algorithms in their many guises. To integrate these multiple approaches, data curation and standardisation will be critical – as is the need to expand efforts beyond our current protein-centric view to the other (macro)molecules that sustain life.

Micronuclei (MN) form from missegregated chromatin that recruits its own nuclear envelope during mitotic exit and are a common consequence of chromosomal instability. MN are unstable due to errors in nuclear envelope organization and frequently rupture, leading to loss of compartmentalization, loss of nuclear functions, and major changes in genome stability and gene expression. However, recent work found that, even prior to rupture, nuclear processes can be severely defective in MN, which may contribute to rupture-associated defects and have lasting consequences for chromatin structure and function. In this review we discuss work that highlights nuclear function defects in intact MN, including their mechanisms and consequences, and how biases in chromosome missegregation into MN may affect the penetrance of these defects. Illuminating the nuclear environment of MN demonstrates that MN formation alone has major consequences for both the genome and cell and provides new insight into how nuclear content is regulated.

RNA vaccines have made evident to society what was already known by the scientific community: nucleic acids will be the “drugs of the future.” By modifying the genome, interfering in transcription or translation, and by introducing new catalysts into the cell or by mimicking antibody effects, nucleic acids can generate therapeutic activities that are not accessible by any other therapeutic agents. There are, however, challenges that need to be solved in the next few years to make nucleic acids usable in a wide range of therapeutic scenarios. This review illustrates how simulation methods can help achieve this goal.